Methods and systems useful for identifying the most influent social media users in query-based social data streams

ABSTRACT

The present invention relates to novel methods and implementing systems that afford a user the ability to analyze a certain stream of data, e.g., independent of size, and identify the people that influence the conversation.

RELATED APPLICATIONS

This application claims the benefit of priority from U.S. ProvisionalPatent Application No. 61/640,137, filed on Apr. 30, 2012, the entiretyof which is incorporated herein by reference.

BACKGROUND OF THE INVENTION

In the last 10 years social media has effectively changed the way peoplecommunicate online. Millions of blogs, or services like Twitter andFacebook enabled new ways of expression for people worldwide. Billionsof messages, blog posts, pictures, videos etc are published on a dailybasis. As such, it has become very important for companies to find a wayto analyze this real-time stream of information.

Many companies use a social monitoring service like uberVU to collectall mentions of a specific brand, a product, or an event. The way thisusually works is by creating a persistent search, which will constantlycollect and analyze all the new mentions of a specific brand, a product,or an event. However, for most of these searches there is a hugequantity of data that needs to be analyzed, making it very difficult fora company to identify the people that they should be interacting with,and to analyze how the messages virally spread in the social space.

There are providers of influence metrics, which offer systems thatanalyze all the data streams from one or more identified users, andcompute a general influence rank. These systems may be capable ofoffering additional deeper analysis; such as identifying the main themesof influence for the users they rank.

The problem with this approach is that it is very difficult to use thesame influence metric for all data streams. For example, maybe a user isvery popular and influent when the user talks about cars, but the user'sinfluence related to a certain food ingredient is close to 0. Moreover,the existing influence metrics require a prior identification of aninfluent user, and measure extent of this influence only after suchidentification.

As such, there is a need for a system that is capable of analyzing largedata sets from social monitoring to detect the most influent socialmedia.

SUMMARY OF THE INVENTION

The present invention relates to methods and systems to identify themost influent social media users for any query-based social data stream(e.g., key words and phrases, search terms, search results, orcombination thereof, including semantic matches thereof). In particular,the methods and systems are able to analyze a certain stream of data,e.g., independent of size, and identify the people that influence theconversation.

Accordingly, one aspect of the invention provides a system comprising amachine-readable medium having instructions stored thereon for executionby a processor to perform a method for identifying the most influentsocial media users in query-based social data streams comprising thesteps of interfacing with one or more streaming social data sources;collecting one or more mentions that match a search query; storing saidmentions on a first server; enhancing said mentions with additionalinformation to produce enhanced mentions; storing said enhanced mentionson a second server; filtering said enhanced mentions into filteredstreams; creating a conversation graph from said filtered streams;calculating influence scores from said conversation graph; andoutputting the most influent social media users.

In another aspect, the present invention provides a method foridentifying the most influent social media users in query-based socialdata streams comprising the steps of interfacing with one or morestreaming social data sources; collecting one or more mentions thatmatch a search query; storing said mentions on a first server; enhancingsaid mentions with additional information to produce enhanced mentions;storing said enhanced mentions on a second server; filtering saidenhanced mentions into filtered streams; creating a conversation graphfrom said filtered streams; calculating influence scores from saidconversation graph; and outputting the most influent social media users,such that the most influent social media users for the streaming socialdata sources are identified.

BRIEF DESCRIPTION OF DRAWINGS

FIG. 1 depicts the flow of data from social media through a system ofthe present invention using a method of the present invention.

FIG. 2 depicts the identification of the most influent people from adata stream along with the relevant and useful information describedherein.

FIG. 3 depicts one example of a conversation graph created in accordancewith the methods of the present invention.

DETAILED DESCRIPTION OF THE INVENTION

The present invention provides novel methods and implementing systemsthat afford a user the ability to analyze a certain stream of data,e.g., independent of size, and identify the people that influence theconversation. In particular, the novel methods and systems are analyzingthe engagement of users as the users relate to a specific persistentsearch, and computes an influence ranking system based on the exposureof a user's message, or how “viral” is a message of a user.

The present invention, including novel methods and systems will bedescribed with reference to the following definitions that, forconvenience, are set forth below. Unless otherwise specified, the belowterms used herein are defined as follows:

I. DEFINITIONS

As used herein, the term “a,” “an,” “the” and similar terms used in thecontext of the present invention (especially in the context of theclaims) are to be construed to cover both the singular and plural unlessotherwise indicated herein or clearly contradicted by the context.

The language “and/or” is used herein as a convention to describe either“and” or “or” as separate embodiments. For example, in a listing of A,B, and/or C, it is intended to mean both A, B, and C; as well as A, B,or C, wherein each of A, B, or C is considered a separate embodiment,wherein the collection of each in a list is merely a convenience.

The language “application programming interface (API)” is art-recognizedto describe a protocol intended to be used as an interface by softwarecomponents to communicate with each other.

The term “interfacing” as used herein, for example in the expression“interfacing with one or more streaming social data sources,” describesthe means of communication between two entities, for example a systemand a data source. In certain embodiments, the interfacing may bebi-directional. In other embodiments, the interfacing may beuni-directional. In particular embodiments, such interfacing may includereceiving, responding, and/or assigning an answer to a request.

The language “machine-readable medium” is art-recognized, and describesa medium capable of storing data in a format readable by a mechanicaldevice (rather than by a human). Examples of machine-readable mediainclude magnetic media such as magnetic disks, cards, tapes, and drums,punched cards and paper tapes, optical disks, barcodes, magnetic inkcharacters, and solid state devices such as flash-based, SSD, etc.Common machine-readable technologies include magnetic recording,processing waveforms, and barcodes. In particular embodiments, themachine-readable device is a solid state device. Optical characterrecognition (OCR) can be used to enable machines to read informationavailable to humans. Any information retrievable by any form of energycan be machine-readable. Moreover, any data stored on a machine-readablemedium may be transferred by streaming over a network.

The language “most influent” social media users” is used herein todescribe users of social networks whose activity can influence the otherusers in the network. The influence is limited to the actions allowed inthose certain social networks and may include, but are not limited to,posting/sharing/commenting on certain messages, following or befriendingcertain users or voting/liking certain messages. In certain embodiments,these users are the at the center of or are considered to be driving theconversation, whose message is being read, shared, discussed, orengaged.

The term “operator” is used herein to describe any person or entity thatengages a system or utilizes a method of the invention, e.g., bydefining a query-based social data streams.

The language “uniform resource identifier (URI)” is art-recognized todescribe a string of characters used to identify a name or a webresource.

The language “Uniform Resource Locator (URL)” is art-recognized todescribe a formatted text string used by web browsers, email clients andother software to identify a network resource on the internet.

The term “user” and “social media user” are used interchangeably hereinto describe any person that interacts with social media, e.g., an authoror someone that increases exposure of the authors work through socialmedia.

The term “spike” is used herein describes a social media insight thatrepresents a sudden increase in social media activity, e.g., data volumefor a given stream and/or attributes combination. In one example, thestream tracking a company's competitor is having a large spike innegative mentions, which means massive bad publicity for the competitor;which could mean a potential business opportunity for a company (e.g.,how News.me leveraged the closedown of Google Reader in order to gain alarger userbase).

The term “burst” is used herein to describe a spike that shows sustainedover-the-average activity. For example, spikes, which are not sovolatile (such as an intelligent political move on social media), mayturn into bursts of sustained large activity showing potential futuretrends.

The language “top story” is used herein to describe an online resourcefor which there is a large spike in social media activity within a datastream. For example, if a newspaper has published a review of a product,and that review goes viral (e.g., people start sharing it, etc.), itwill show up as a top story in the product's stream.

II. METHODS OF THE INVENTION

In one embodiment, the present invention provides a method foridentifying the most influent social media users in query-based socialdata streams comprising the steps of

interfacing with one or more streaming social data sources;

collecting one or more mentions that match a search query;

storing said mentions on a first server;

enhancing said mentions with additional information to produce enhancedmentions;

storing said enhanced mentions on a second server;

filtering said enhanced mentions into filtered streams;

creating a conversation graph from said filtered streams;

calculating influence scores from said conversation graph; and

outputting the most influent social media users, such that the mostinfluent social media users for the streaming social data sources areidentified.

In another embodiment, the method further comprises the step ofidentifying an influent social media user.

In another embodiment, the method further comprises the step ofreporting one or more of the most influent social media users.

In certain embodiments, the first server is the same as the secondserver.

In certain embodiments, the step of collecting the mentions furthercomprises establishing a search inquiry, e.g., an iterative searchquery.

In certain embodiments, the step of collecting the mentions may belimited by prescribed time interval or may be continual.

In certain embodiments of the invention, the data stream comprisesgreater than 500,000 mentions, e.g., greater than 1,000,000 mentions,e.g., greater than 2,000,000 mentions, e.g., greater than 5,000,000mentions, e.g., e.g., greater than 10,000,000 mentions.

A. Data Collection

In the methods of the present invention, the data analyzed is in theform of mentions or messages, which are used interchangeably herein. A“mention” or “message” is a post originated on a social media platform,and associated information. Mentions are usually collected by matchingcertain keywords using queries or by matching certain mentionsparameters. In certain embodiments, a mention may comprise an author; adate, a main message (depending on the type this can be text, image,video, audio or a combination thereof). In a particular embodiment, themention may comprise a location where the mention was created (either asGPS coordinates or text), a comment thread, and/or the number and listof people who liked or favorited the mention.

Collection of all the mentions from a query-based social data stream,i.e., derived from data streams that match a specific query (e.g., keywords and phrases, search terms, search results, or combination thereof,including semantic matches thereof) may be made from any availablesocial network. This step of collection ensures that the stream beinganalyzed is related to the topic of interest, i.e., the query, toidentify influencers.

The sources of data may be any social network (e.g., Twitter, Facebook,Google+, Pinterest, Instagram, LinkedIn etc) or websites where an authorcan be uniquely identified (e.g., blogs, boards, etc). Access to thisdata may be acquired in a variety of ways. In certain embodiments, thedata may be acquired through data partnerships with the main socialmedia players or data reseller, publicly available API (applicationprogramming interface), and/or scraping or other forms of HTMLmanipulation.

In certain embodiments, information related to authors may be compriseone or more of the following: a name: either as a nickname or full name(real or invented), a picture or a set of pictures, a description,and/or a list of other website and social profiles the user can be foundon. In alternative embodiments, information related to authors may becomprise a URI (uniform resource identifier).

The amount of data collected may depend upon the slice of the timeline,or time interval, which is being analyzed. For example, it could be aday, a week or a month. In certain embodiments, for real-time resultsthe slice should be as small as possible, probably hours.

In certain embodiments, the invention is supported by methods andsystems that allow queries from different operators to be saved &managed. In certain embodiments, a mechanism to collect data that matchthose queries may be included, and can be part of the same backendsystem. In particular embodiments, the mentions collected for thosequeries may also be annotated with meta-information, e.g., as much aspossible; including the location, the language or the platform of themention. In a specific embodiment, the back end system may createarchives of all the mentions collected. In such cases, a record of allthe messages posted about a specific topic is presented, and thehistorical record may be as long as the period needed for which toprovide influence.

B. Data Enhancement

In the methods of the present invention, the collected mentions areenhanced. Such enhancements are applied on mentions to provideadditional information to be stored on the server along with thementions, i.e., producing enhanced mentions, and assist in filtering thedata. In certain embodiments, these enhancements may include one or moreof the following: the sentiment of the mention, i.e., whether the authorhas a positive, negative or neutral opinion on the subject indiscussion; the language of the mention; the location: city, country;the gender of the author; the potential reach of each mention, i.e., howmany people will read it; and the following of an author.

In the methods of the present invention, the data is filtered intostreams, e.g., using the enhancement added to the mention. Theimportance of this filtering is it separates the global conversationinto a meaningful subset. In certain embodiments, the stream is orderedby time, i.e., meaning the most recent mentions are at the top. Eachenhanced mention is filtered, and will have some detectors, or filters,that will go through each to create a filtered stream, which may beanalyzed to provide statistical data, e.g., numerically or graphical innature.

Mentions and streams are saved, e.g., in databases, on a server. Thetypes server and/or the database depend on the volume of data and thedesired use of the data, e.g., wherein the data is accessed by aspecific timeframe. Moreover, two pieces of information on whichcomputation of social spikes may be based, may be stored on the server:the raw mention data, and the statistics gathered from those mentions.

In certain embodiments, the method is capable of detecting peopleinfluencing the social conversation for a specific, topic. This isachieved by filtering the conversations from which the mentions deriveinto streams. The most popular filtering stream is by keyword matchingusing boolean operators. For example, the topics for filtering may beselected from one or more of brand names, product names, names ofpersons, campaigns or events, and market terms. Moreover, in certainembodiments, the streams can be further filtered down using genericterms such as: location, e.g., just for mentions in Brazil; language,e.g., people speaking Japanese; social network, e.g., on Twitter( )sentiment, e.g., people saying nice things about Xbox®; and gender,e.g., women discussing the new maternity laws.

C. Conversation Graphs

Based on the mentions in a certain filtered stream, a conversation graphis created. This conversation graph is stream-specific, affording theability to filter influencers by their topical influence score insteadof a global one. Moreover, the nodes in the graph represent authors ofthe mentions collected in each filtered stream. In certain embodiments,such node may contain multiple identities of the same person (if user Xidentified on Twitter is the same as user Y on LinkedIn). FIG. 3 showsone example of a conversation graph created in accordance with themethods of the present invention.

For each message the relationship with the previous and subsequentmessages is identified. A message “linking” to another user's message(post, blog, video etc) may be considered a “vote” for that first user'smessage. For example, a link from message A (post, blog, video etc) tomessage B may be interpreted as a vote, by message A, for message B. Incertain embodiments, the analysis considers sheer volume of votes aswell as the weight of the message that casts the vote. See, for example,FIG. 1.

A line pointing from A to B means the A amplified the message posted byB by a form of social sharing. Social sharing can differ from socialnetwork to social network and these actions can come and go as newsocial networks emerge or old social networks shut down. Thisamplification, for example, may be result in a new mention, or justamplify an already existing mention by exposing it to new audiences,which may be traced back to another mention that has another author.

Each social action has a certain weight depending on the importance ofthe action. For example a favoriting a tweet is less impactful thatretweeting that tweet because the audience in the later case is bigger.The weights of those actions can and will change all the time as thesocial networks change their functionality and the number of users ineach social networks goes up or down. In particular embodiments, newactions can be added or removed as the usage of certain social networksincreases or decreases.

The nodes on the conversation graph, or authors of the mentions, areconnected by directed weighted edges. Such nodes are added/removedwhenever a new author is detected in the context of the stream. Thisauthor will then have a timeframe in which to prove his/her influence,e.g., to an algorithm, or else be removed, e.g., and then re-added againlater, when the next action related to the author occurs. New edges areadded whenever a user A amplifies a message of another user B for thefirst time, and the edge contains information related to the strength ofthe amplification and when it took place. Similar to nodes, thefaithfulness of user A with respect to user B has to be shown constantlyby amplifications in order for the edge to remain in the graph,otherwise it will fade out in time. Nodes may be updated whenever newinformation is gathered about the authors they represent; this can meanupdates due to the number of social actions (the author has published anew message), or even due to the properties of the author (name haschanged, or the number of followers). Edges may be updated either by thepassage of time (they fade away), or by new information supporting thefaithfulness of users which amplify the social actions of other users.

In certain embodiments, a link may be selected from a share on Facebookor similar social networks; a thumbs up, like or retweet; the sharing ofthe same resource by user A, who is following user B (and posted theresource earlier); or friending of following a user. In this respect,not all the links must cast the same voting score, i.e., some may bemore valuable than others.

In certain embodiments, a link may be selected from one or more examplesusing today's social networks: a tweet mentioning another user (e.g.,Twitter); a retweet of a mention (e.g., Twitter); favoriting a mention(e.g., Twitter); liking a post (e.g., Facebook); sharing a post (e.g.,Facebook, Google+, or LinkedIn); +1 on a post (e.g., Google+); posting alink to a blog (e.g., any social network); and commenting (e.g., almostany social network).

The size of the graph will depend on the timeframe to detect influencersfor and the size of the stream. For example, for a very large stream itmay be useful to analyze the last day, and thus create a graph based onthe last 24 hours. Alternatively, for a small stream it may be moreuseful to analyze influencers for a 7 days window. In certainembodiments, the timeframe, or time interval, may be established fromthe outset, during the process, or may be pre-programmed into the systemimplementing the method.

In certain embodiments, the graph may be continuously updated, e.g., asthe system gathers mentions from around social media, i.e., filtered bythe current stream. As such, referring back to the example where A linksto B example: A links to B means A increased the reach of the message Bcreated. Both nodes and edges within this graph are filtered in thecontext of the stream. This means that it's possible for the same user Ato appear in two graphs for two different streams with completelydifferent roles and influence (e.g. where A might be an influencer forstream S1 and not for S2).

Each message, linked to an author, is assigned an influence rank thatvaries with time. Based on the author's history within the scope of thatsearch we compute an influence rank for the authors.

D. Calculating Influence Scores

Influence scores may be computed based on the previously mentionedgraph, by an algorithm which is triggered based on the volume of thestream. If a smaller stream is being analyzed, it may be triggered inreal-time; while for larger streams batches of mentions may be collectedin order to process them as a group.

The algorithm examines the graph iteratively and assigns an influencescore to each node (author) at each step. This influence score isspecific to the current stream and is evolved/resolved by a series ofiterations until the score is stable. The number of iterations isproportional to the maximal chain of consecutive elements found in thegraph, e.g., a recursive algorithm.

Each iteration of the algorithm evolves these scores by computing theoutreach of an author A towards the rest of the graph, given theindividual probabilities found on each edge that the next connected nodewill amplify an action of A. This probability (similar to faithfulness)is computed based on the interactions between the two users A and Bdirectly connected by an edge. Since interactions between social mediaauthors are very dynamic, meaning new messages between them happen allthe time, in certain embodiments, the graph is refreshed and the scoresare refreshed to reflect the changes, e.g., in a continual fashion.

As an example: if A is a potential influencer, and B1, B2, B3 haverecently amplified the social actions of A, based on those interactions(their strength and frequency) we are able to compute the probabilitythat they will amplify again a new message from A for the currentstream.

At the finish of all iterations, a stable influence score will beassigned for each author (e.g., with a value between 0 and 1) which willmeasure the potential for amplification of a new message of the author.

E. Outputting Influencers

Once the algorithm is triggered, and stable influence scores arecomputed for all authors within the graph, the top users may then beextracted. In a particular embodiment, the extracted top users may beannotated with visual information that justifies its influence score,e.g., selected from one or more of the following: a user picture andpersonal information, if available (this includes profiles on all knownsocial networks, for example); users amplifying the actions togetherwith information on the “faithfulness” (how probable is an user toamplify a new social action of the influencer); or other contextualinformation determined by our system and related to the current stream,such as a topic cloud. In specific embodiments, these annotations may bestored in a database on a server, e.g., in a contextual manner (a usercan have multiple entries for multiple streams), and may be displayed tojustify the choice of the influencer. See, for example, FIG. 2.

In certain embodiments, influencers may be displayed in a graphical userinterface (GUI) where users can select which influencers they areinterested in, based on streams, group of streams, or influence score.

Operators of the system may also specify both volatile filteringcriteria in order to explore the existing influencers, and persistentones, causing certain classes of influencers to never be generatedagain.

In certain embodiments, the methods may be used to filter a social mediastream based on an influence threshold. For example, out of all themessages collected for a certain persistent search only the authors andmessages that exceed the threshold will be reported. In particular, thiscould be very useful for an operator to identify: who are thepersons/users driving the discussion forward and use the information toengage with them (e.g., to reward them, or to send them products forreviews etc); to identify complaints from influent people/users who cangenerate a lot of negative sentiment towards a brand, product etc; or toidentify the people/users who drive the most buzz in a marketingcampaign.

In certain embodiments, the methods may be used to use the influencerank to augment the profile that are usually stored in a customerrelationship management system (CRM). Each customer, lead or contact ina CRM system can have a influence rank attached to it so a sales orsupport person will know how to generate buzz for a topic.

In certain embodiments, the methods may be used to get analytics like: achart with the most influent users and messages. The chart will changedepending on the time selection (a chart for the last month can be verydifferent from one for the last year) and can be further filtered, e.g.,based on social network, geolocation (country, state, city), language,gender or other various demographics factors.

In certain embodiments, the methods may be used to generate a web chartshowing the distribution of messages from the most influent users to theless influent ones. The distribution chart can be time based (and willlook like a timeline), location based (it will look like a heat-map)etc.

In certain embodiments, the methods may be used to generate a graphshowing the average influence of the top 10 users for that searchstream. This would be a good indicator of the level of influence a topicattracts, e.g., and the number can be use to compare two similar searchstreams

III. SYSTEMS OF THE INVENTION

In another embodiment, the present invention provides a systemcomprising a machine-readable medium having instructions stored thereonfor execution by a processor to perform a method for identifying themost influent social media users in query-based social data streamscomprising the steps of

interfacing with one or more streaming social data sources;

collecting one or more mentions that match a search query;

storing said mentions on a first server;

enhancing said mentions with additional information to produce enhancedmentions;

storing said enhanced mentions on a second server;

filtering said enhanced mentions into filtered streams;

creating a conversation graph from said filtered streams;

calculating influence scores from said conversation graph; and

outputting the most influent social media users.

In another embodiment of the system, the method further comprises thestep of identifying an influent social media user.

In another embodiment of the system, the method further comprises thestep of reporting one or more of the most influent social media users.

In certain embodiments of the system, the first server is the same asthe second server.

In certain embodiments of the system, the step of collecting thementions further comprises establishing a search inquiry, e.g., aniterative search query.

In certain embodiments of the system, the step of collecting thementions may be limited by prescribed time interval or may be continual.

In certain embodiments of the system, the data stream comprises greaterthan 500,000 mentions, e.g., greater than 1,000,000 mentions, e.g.,greater than 2,000,000 mentions, e.g., greater than 5,000,000 mentions,e.g., e.g., greater than 10,000,000 mentions.

In certain embodiments, the system affords the ability to accept inputfor the purpose of marking a item of data as not relevant, e.g.,providing the ability of the system to learn with respect to refinementand improved calculations/analysis. For example, in one embodiment suchinput may be priority level indicator.

In certain embodiments, the instructions stored on the machine-readablemedium are online software or offline software.

In certain embodiments, the software is an online application, e.g., aweb-based application or a cloud-based application.

In certain embodiments, the software is an offline application.

In certain embodiments, the data is selected from the group consistingof social network or websites where an author can be uniquelyidentified, and any combination thereof.

In certain embodiments, the machine-readable medium is selected from thegroup consisting of magnetic media, punched cards, paper tapes, opticaldisks, barcodes, magnetic ink characters, and solid state devices.

The system, in certain embodiments, may be part of a media monitoring,their engagement console, customer relationship management or analyticsservice. Moreover, the system may be a web application accessible in anInternet browser, a desktop software running on Windows, Mac OS, Linux(or any other operating system), or a mobile application (available onsmartphones or tablets).

In certain embodiments, the system can be delivered as an API method,e.g., which may be accessed remotely by any other software.

In certain embodiments, the system may be used as a widget that you canembed in any HTML enabled app. In a particular embodiment, the widgetmay display all the metrics (like top influencers for a topic orinfluence distribution map etc). In a specific embodiment, the widgetlayout may be customized to fit the application or page in which it isembedded.

INCORPORATION BY REFERENCE

The entire contents of all patents, published patent applications andother references cited herein are hereby expressly incorporated hereinin their entireties by reference.

EQUIVALENTS

Those skilled in the art will recognize, or be able to ascertain usingno more than routine experimentation, numerous equivalents to thespecific procedures described herein. Such equivalents were consideredto be within the scope of this invention and are covered by thefollowing claims. Moreover, any numerical or alphabetical rangesprovided herein are intended to include both the upper and lower valueof those ranges. In addition, any listing or grouping is intended, atleast in one embodiment, to represent a shorthand or convenient mannerof listing independent embodiments; as such, each member of the listshould be considered a separate embodiment.

What is claimed is:
 1. A system comprising a machine-readable mediumhaving instructions stored thereon for execution by a processor toperform a method for identifying the most influent social media users inquery-based social data streams comprising the steps of interfacing withone or more streaming social data sources; collecting one or morementions that match a search query; storing said mentions on a firstserver; enhancing said mentions with additional information to produceenhanced mentions; storing said enhanced mentions on a second server;filtering said enhanced mentions into filtered streams; creating aconversation graph from said filtered streams; calculating influencescores from said conversation graph; and outputting the most influentsocial media users.
 2. The system of claim 1, wherein the instructionsstored on the machine-readable medium are online software or offlinesoftware.
 3. The system of claim 1, wherein the software is an onlineapplication.
 4. The system of claim 3, wherein the software is aweb-based application.
 5. The system of claim 3, wherein the software isa cloud-based application.
 6. The system of claim 1, wherein thesoftware is an offline application.
 7. The system of claim 1, whereindata is selected from the group consisting of social network or websiteswhere an author can be uniquely identified, and any combination thereof.8. The system of claim 1, wherein the machine-readable medium isselected from the group consisting of magnetic media, punched cards,paper tapes, optical disks, barcodes, magnetic ink characters, and solidstate devices.
 9. The system of claim 1, wherein the method furthercomprises the step of identifying an influent social media user.
 10. Thesystem of claim 1, wherein the method further comprises the step ofreporting one or more of the most influent social media users.
 11. Thesystem of claim 1, wherein the first server is the same as the secondserver.
 12. The system of claim 1, wherein the step of collecting thementions further comprises establishing a search inquiry.
 13. The systemof claim 1, wherein the step of collecting the mentions may be limitedby prescribed time interval or may be continual.
 14. A method foridentifying the most influent social media users in query-based socialdata streams comprising the steps of interfacing with one or morestreaming social data sources; collecting one or more mentions thatmatch a search query; storing said mentions on a first server; enhancingsaid mentions with additional information to produce enhanced mentions;storing said enhanced mentions on a second server; filtering saidenhanced mentions into filtered streams; creating a conversation graphfrom said filtered streams; calculating influence scores from saidconversation graph; and outputting the most influent social media users,such that the most influent social media users for the streaming socialdata sources are identified.
 15. The method of claim 14, wherein themethod further comprises the step of identifying an influent socialmedia user.
 16. The method of claim 14, wherein the method furthercomprises the step of reporting one or more of the most influent socialmedia users.
 17. The method of claim 14, wherein the first server is thesame as the second server.
 18. The method of claim 14, wherein the stepof collecting the mentions further comprises establishing a searchinquiry.
 19. The method of claim 14, wherein the step of collecting thementions may be limited by prescribed time interval or may be continual.20. The method of claim 14, wherein data is selected from the groupconsisting of social network or websites where an author can be uniquelyidentified, and any combination thereof.